Reward:
Inspired by [1]} and [2]}, the reward \(r_t\)  at a state \(s_t\)  is defined using a biaffine attention technique to model the likelihood of \(m_j\)  as an antecedent of \(m_i\) , by jointly considering the distance between two mentions. The reward is hence defined as:
\(\begin{split}r_t &= e^{-\gamma \parallel i-j \parallel }(v_m^Tf_{nn}^1(m_i)+v_m^Tf_{nn}^1(m_j)\\&+f_{nn}^2(m_i,m_j,m_i\circ m_j)+v_{bi}^Tf_{nn}^3(m_i))\end{split}\) 
